Stratya2D: Enhancing kinematic backstripping through image-based 2D horizon integration
The study of sedimentary basins is crucial for understanding Earth’s evolution and geological history. Traditional basin analysis, often constrained by 1D subsidence analysis, limits the spatial understanding of geological processes. This study introduces Stratya2D, a Python-based tool that extends traditional methodologies by extending 1D decompaction and backstripping to a 2D framework allowing for detailed basin analysis. The tool extracts horizon annotations from pre-interpreted seismic images, enabling coordinate-based reconstruction of depositional surfaces. Using advanced image processing techniques, Stratya2D integrates horizon extraction, depth normalisation, and Monte Carlo Simulation (MCS) to quantify uncertainties in tectonic subsidence and layer evolution at each time step, offering a breakthrough in geoscientific analysis. This innovative approach offers a more cost-effective alternative to traditional software and improves prediction reliability. The tool’s effectiveness was validated through comparisons with established literature and specific case studies, including data from the NDI Carrara 1 well in the South Nicholson region, Northern Territory, Australia, along the 17GA-SN1 seismic line. The results closely align with previously published data and PetroMod simulations, accurately replicating the tectonic subsidence curve and offering extended insights into the complex geological context of the South Nicholson Region. Comparative analysis with PetroMod confirms the robustness of Stratya2D, while the inclusion of MCS highlights the critical role of uncertainty quantification in subsurface modelling. Stratya2D offers a robust and versatile tool for regional-scale basin modelling, effectively addressing diverse geoscientific challenges. • Visualises 2D basin evolution using limited well and seismic data. • Extracts geological horizons from images with advanced image processing. • Integrates results at the well location and normalises results across the seismic section. • Enhances sedimentary process analysis with horizon flattening.
Authors
- Patrick Makuluni (ORCID: https://orcid.org/0000-0003-0552-2396)
- Juerg Hauser (ORCID: https://orcid.org/0000-0002-8964-7331)
- Harikrishnan Nalinakumar (ORCID: https://orcid.org/0000-0003-1700-0106)
- Stuart Clark (ORCID: https://orcid.org/0000-0002-0495-670X)
Publication Details
- Journal
- Computers & Geosciences
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1016/j.cageo.2025.106056
- Primary Topic
- Robotics and Sensor-Based Localization
- Type
- article
- Field-Weighted Citation Impact
- 0.00